In the automotive and storage and distribution industries, effective inventory management applications are critical to daily operations, where locating vehicles, trailers, or other mobile inventory accurately and in a timely manner is critical to business success.
At automotive dealerships, both sales and service employees routinely search for vehicles. If a salesperson cannot locate a vehicle for a prospective customer to test drive within a timely manner, the salesperson is less likely to sell a vehicle. On the service side, since a service technician bills customers for time spent repairing a vehicle, any time spent locating a vehicle is lost revenue. If vehicles could be found more quickly, more technician time would be spent repairing vehicles, which would increase the revenue per service technician for the dealership. In the retail automotive industry, lost vehicle sales and inefficient use of labor are the costs of not locating vehicles accurately and quickly.
In the storage and distribution industry, distribution centers maximize revenue by quickly transloading containers to trailers, splitting container loads amongst multiple trailers, and returning containers back to ports in a timely manner. Centers are typically penalized for delays, and earn more business from retailer and other customers if they are consistently on-time. Similarly, warehouses maximize revenue by quickly tracking boxes and pallets for packing and shipping. Locating mobile objects such as trailers, containers, boxes, and pallets accurately and quickly is critical to performing any of these tasks.
In fact, in both the automotive and storage and distribution industries, the problem of locating mobile objects increases in difficulty as a function of the total number of objects in inventory and the number of possible locations.
Currently, inventory management applications display the location information in a graphical form (on a GUI) of single or multiple objects by showing an image, (typically a dot or other image representing the object), against a background image of an entire map of the storage facility. Multiple objects showing multiple images are not used often because of the difficulty of interpreting raw location information from multiple objects, simultaneously. The object image is placed on the digital map on the basis of raw (x,y,z) coordinates generated through one or multiple underlying object location technologies, including GPS, RFID, RF, Ultrasonic, Acoustic, Infrared, and other technologies.
Depending on the precision of the underlying location technology, there is a resulting error radius which is expected to circumscribe the actual location of the object. This error radius may vary based on environmental constraints, including signals being blocked by walls, buildings, other objects, or inclement weather. A user is expected to extrapolate the most likely actual location of the object, based on the dot image and the error radius.
However, particularly when groups of vehicles or other objects are crowded together, the inherent location errors may result in multiple overlaps and fuzzy images which can be difficult to interpret by an observer and lead to errors in guessing actual locations which become time consuming and costly.
For example, in a parking lot, if the vehicle being sought is blocked by 2 other vehicles, an accurate multiple object display of all vehicles would prepare the user to take the keys of all 3 vehicles in order to retrieve the vehicle being sought in a single step. Alternatively, in a single object display, the obstructing vehicle would not be displayed and it would have been necessary for the user to have physically gone to the vehicle, noted the 2 vehicles that are blocking the vehicle being sought, returned to the building to pick up all 3 keys, and then returned to the vehicle to retrieve it, requiring an additional round trip.
Similarly, in a warehouse, if a user is searching for a single product that is on one of multiple pallets located throughout the warehouse, the user can choose to retrieve the pallet that is least encumbered by other pallets. Alternatively, in a single object display, the user would arbitrarily choose a pallet, possibly requiring unnecessary time and work to move other pallets out of the way before being able to retrieve the pallet being sought.
Furthermore, in traditional location technology systems that use principles of triangulation, trilateration or multilateration, all reference points used in determining the position of an object are assigned equal priority.
For example, when an object is located via GPS, the GPS receiver monitors satellite messages without any specific priority assigned to one satellite over another. All information compiled from reference points is assumed to have a high level of precision. In particular, satellites continually transmit messages that include the time the message was transmitted and precise orbital information. When a receiver receives a message from a satellite, the receiver determines transit time which is directly correlated to the distance from the satellite. The basis of calculating transit time relies on a high precision, synchronized clocking system and the universal speed constant of light.
The methodology of treating all reference points with equal priority may work for certain location technology systems where there is a high level of innate precision throughout stemming from the technology used and the basic laws of physics.
However, an example of a location technology system where assigning equal priority to reference points is not favorable is a sensor network where received signal strength or power, as measured in dBm, of multiple radio signals are inputs in a trilateration algorithm. Current approaches assign equal priority to all reference points, but because signal strength received from a reference point is intrinsically fuzzy, calculations of precise location are challenging. Many significant obstacles are encountered: radio signals exhibit exponential decay and suffer/experience reflection, interference, attenuation or general noise. These problems are exacerbated by the environment where the location technology system is situated, for example, variable ambient air quality—variations in air-borne particulates and in humidity. Furthermore, at automotive dealership premises, there can be RF reflection from a vehicle's metal panels and pillars, frequency interference from WIFI and other radio/electromagnetic wave signals, and signal attenuation from physical/mechanical obstructions (e.g. walls, equipment). Although, GPS signals may also experience from some of these obstacles, they do not suffer from an exponentially decaying radio signal of 2.4 GHz, illustrated, for example in FIG. 15, (RFID transmitter).
The received signal strength (RSSI) is a function of the transmitted power and the distance between the reference point/sender and the receiver. The received signal strength will decrease with increased distance as the equation below shows.RSSI=−(10n log10d+A)
n: signal propagation constant, also named propagation exponent (empirically generated)
d: distance from sender/transmitter
A: received signal strength at a distance of one meter (empirically generated)
As can be seen from the graph, the further a receiver is from a reference point the less variance there will be in RSSI. This decay, coupled with imprecision arising from reflection, interference, attenuation or general noise, greatly reduce accuracy.